The Long-Term ST Database contains 86 lengthy ECG
recordings of 80
human subjects, chosen to exhibit a variety of events of ST segment
changes, including ischemic ST episodes, axis-related non-ischemic ST
episodes, episodes of slow ST level drift, and episodes containing
mixtures of these phenomena. The database was created to support
development and evaluation of algorithms capable of accurate
differentiation of ischemic and non-ischemic ST events, as well as
basic research into mechanisms and dynamics of myocardial ischemia.

Half (43) of these 86 recordings, representing
42 of the 80 subjects, were contributed to PhysioNet by the
creators of the database in February 2003, and the remaining half of the
database was contributed in May 2007. (A corrected version of s30801.dat
was also posted together with the second half of the database.) Detailed clinical notes and ST deviation
trend plots are provided for all 86 records. The entire Long-Term ST
Database is also available from its original home page at the
Laboratory for
Biomedical Computer Systems and Imaging at the University of Ljubljana,
Slovenia.

The individual recordings of the Long-Term ST Database are between 21
and 24 hours in duration, and contain two or three ECG signals. Each
ECG signal has been digitized at 250 samples per second with 12-bit
resolution over a range of ±10 millivolts. Each record
includes a set of meticulously verified ST episode and signal quality
annotations, together with additional beat-by-beat QRS annotations and
ST level measurements.

For each recording, the first digit in the record name (2 or 3) indicates
the number of ECG signals. Records obtained from the same subject have
names that differ in the last digit only.

Each record is represented by 12 files, all with the same base name (the record
name) and a suffix that identifies the file type:

The measurements in the .16a files were used to construct ST level and
deviation functions for each signal, as recorded in the .stf files.
(Further details about the .stf, tsr.zip, and
.klt.zip files are available here.) ST
episodes were identified independently for each signal, based on its ST
deviation function and on these criteria:

An episode begins when the magnitude of the ST deviation function
first exceeds 50 µV;

The deviation must reach a magnitude of Vmin or more throughout a
continuous interval of at least Tmin;

The episode ends when the deviation becomes smaller than 50 µV,
provided that it does not exceed 50 µV in the following 30 seconds.

Since differing criteria may be appropriate depending on the application,
three sets of ST episode annotations are provided. The annotation codes
used in the .sta, .stb, .stc, and .16a
files are described here.

For each record, the numbers of ST episodes as determined by each of the three
sets of criteria are summarized in an additional text file (with suffix
.cnt). The deviation functions and the locations of the episodes are
presented graphically in a set of trend plots here. Each
record is represented by a 24-hour plot (_00-24.png) and by five
6-hour plots which overlap by one hour (_00-06.png,
05-11.png, etc.).

Development of the Long-Term ST Database was an inter-institutional and
international effort coordinated by Prof. Franc Jager of the Faculty of
Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Other investigators include: Roman Dorn, PhD, and Ales Smrdel, MSc, of the
Faculty of Computer and Information Science, Ljubljana; Dr. Gorazd Antolic of
the University Medical Center, Ljubljana, Slovenia; Drs. Alessandro Taddei and
Michele Emdin of the CNR Institute for Clinical Physiology (the creators of the
European ST-T Database European ST-T Database), Pisa, and
Prof. Carlo Marchesi of the University of Firenze, Firenze, Italy; and
Dr. Roger Mark and George Moody of the Massachusetts Institute of Technology
(the creators of the MIT-BIH Arrhythmia Database),
Cambridge, MA, USA, and the Beth Israel Deaconess Medical Center, Boston, MA,
USA. The project was supported by Medtronic, Inc. (Minneapolis, MN, USA) and
Zymed, Inc. (Camarillo, CA, USA). Development of the Long-Term ST Database
began in 1995 and was completed in 2002. We thank all who contributed to this
project; further details are here.

Several sources contributed recordings to the Long-Term ST Database:

Eleven of the recordings included in the Long-Term ST Database are from the
initial Long-Term ST Database developed under a joint U.S.-Slovenian research
project between 1995 and 1998.

Ten additional recordings of the Long-Term ST Database are from the collection
originally gathered by the Pisa group for the European ST-T Database, which
contains two-hour excerpts of some of these same recordings. The original
analog recordings were redigitized for the Long-Term ST Database; since the
signals have been rescaled as a result, direct comparison of the annotations in
the European ST-T Database records with those for the corresponding portions of
the Long-Term ST Database records is not possible. The inclusion of these
recordings in the Long-Term ST Database allows study of the dynamics of
ischemic ST changes over a much longer period in these previously well-studied
subjects. Among the samples available here, record s20021 includes the two-hour
segment that was previously digitized to produce record e0113 of the European
ST-T Database.

Another 18 of the LTSTDB recordings, those containing recordings with three ECG
signals, were contributed to the project by Zymed, Inc.

The annotation of the Long-Term ST Database was performed using SEMIA, a program written by the group
in Ljubljana for this purpose. SEMIA provides an interactive
graphical user interface to a semi-automated algorithm for measurement
of ST levels. Sources for SEMIA, and a precompiled version for GNU/Linux,
are available here (as individual files), and as
a gzip-compressed tar archive.

Each recording was reviewed independently by expert annotators using
SEMIA at each of the three sites (Ljubljana, Pisa, and Cambridge).
Participants met several times annually to obtain the consensus
reference annotations.

A series of SEMIA screenshots illustrates the annotation process. (Use your
browser's Back button to return to this page after following the links to these
screenshots in the next paragraph. If you have problems viewing the
screenshots in your browser, please read this
note.)

The first task faced by the expert annotators was to mark the locations of the
PQ junction (the isoelectric level) and the J point, based on 16-second
averaged cardiac cycles chosen at frequent intervals throughout the recordings.
These marks serve as guideposts for the automated ST level measurement
algorithm that performs the next step. The experts then examine the time
series of ST level measurements in order to locate and to mark a set of local reference points
(marked as LR in the upper panel of the figure). These are used to construct a
piecewise linear baseline ST level function, which may vary over time as a
result of body position changes or other factors unrelated to ischemia,
especially in subjects with prior myocardial infarctions. Axis shifts reflect body
position changes, and are usually most apparent in the QRS complexes (note the
changes in the QRS principal components, KL1 - KL5, in the lower panel of the
figure). By contrast, when ischemic ST changes occur, they are most apparent
in the principal components of the ST segment (see the lower panel in this screenshot).
Local references are placed before and after each such episode, and the
episodes are annotated next. During this process, the expert annotators have
the option of viewing either the ST level time
series or the ST deviation time
series (formed by subtracting the baseline ST level function from the
uncorrected ST level time series), as shown in the upper panels of the two
screenshots. For further details, see reference 4 below.

Software for producing printed
documentation of the Long-Term ST Database is available for Linux or Unix.
The software produces compact trend plots of the ST level and ST deviation
time series, with indicators of ischemic and non-ischemic ST episodes.

Updates

Franc Jager and Miha Amon have contributed additional sets of time
series computed from the ST segments of each normal and non-noisy beat
in the database. In each case, they provided time series computed
separately for each ECG lead.

In 2009, Miha and Franc calculated coefficients of normalized and
non-normalized Legendre orthonormal basis functions. The Legendre
orthonormal-transform coefficient time series are in
the legendre subdirectory.

In 2011, Miha and Franc derived new single-lead KL basis functions
for the ST segments, and used them to compute normalized and
non-normalized KL coefficients. The KL calculated coefficients are
centralized by their mean values. The single-lead KL coefficient time
series are in the kl-single subdirectory.

In 2015, Miha and Franc derived another new set of single lead KL
basis functions for the ST segments, and their subsequent normalized
and non-normalized KL coefficients. This time, the KL coefficients are
not mean-centered. The single-lead KL coefficient time series are in
the kl-single-uncentralized
subdirectory.

Derivation of the Legendre orthonormal-transform normalized and
non-normalized coefficient time series, derivation of new single-lead
KL basis functions for the ST segments, and derivation of normalized
and non-normalized KL coefficient time series is described in
reference 5 below.

The kl-single and kl-single-uncentralized projects use different techniques
(time domain and KL based respectively) to remove noisy heartbeats. Therefore
the KL-Transform is applied on two different covariance matrices derived from
two different sets of ST sections, which results in two slightly different sets
of basis functions. More importantly however is that only the subsequent
kl-single coefficients are centralized by their mean values.